Pass the PMI CPMAI CPMAI_v7 Questions and answers with CertsForce

CPMAI’s Domain VI: Trustworthy AI is designed to span the entire project lifecycle, embedding ethics, transparency, fairness, privacy, and security checks into every phase—from Business Understanding through Operationalization—so that trustworthy practices are not an afterthought but a continuous, integrated activity .

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In CPMAI’s Trustworthy AI framework, Transparent AI focuses on providing clear documentation of data sources, modeling approaches, evaluation methods, and deployment plans so that stakeholders can audit and understand how the system was built. The users’ request for “visibility into all aspects” of model development, training data, and test sets directly maps to the Required AI Transparency Considerations task early in the methodology .

In Phase I’s AI Skills Assessment, CPMAI directs teams to “List the cognitive skills you have available” and to identify “What expertise and skills you have available to you that you can use for this project” as well as any skills gaps to address . The methodology—and the CPMAI Glossary’s definition of a data scientist—emphasizes core competencies in:

Data Engineering & Preparation (manipulating, collecting, transforming large data sets)

Critical Thinking (interpreting insights to align with business goals)

Algorithmic Understanding (selecting and applying the right statistical or ML models)

Mathematical Proficiency (especially statistics and calculus underpinning model creation)

By contrast, prompt engineering (A) is a specialized role for LLM interactions, not a general data-science core competency; and RPA-centric automation skills (E) fall outside the CPMAI focus on cognitive/ML capabilities.

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Phase II: Data Understanding is dedicated to identifying data requirements, collecting initial data, assessing data quality, and verifying that necessary datasets are accessible and fit for modeling. Determining what data you need and confirming access are the core activities of this phase .

The Phase IV: Model Development generic tasks include:

Select Modeling Technique (algorithm selection)

Generate model test design

Model Training / Model Building

Hyperparameter Optimization (model tuning)

Final Model Selection (choosing the best candidate against business criteria) is performed in Phase V: Model Evaluation, not in Phase IV .

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CPMAI’s Domain IV: Data for AI – Task 1: Managing Data Fundamentals and Big Data Concepts emphasizes that leaders—not just technical practitioners—must grasp the core characteristics of Big Data (the V’s: volume, velocity, variety, veracity) and its strategic role in delivering business advantage. Ensuring senior leadership is data literate and understands how to leverage Big Data concepts across teams is critical for sustaining a competitive edge; merely upskilling the technical team or distributing data literacy unevenly will leave strategic gaps.

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CPMAI defines analytics as “the use of statistical and computational methods to extract meaningful insights from data.” When the goal is to discover and highlight patterns and insights within existing datasets, applying data mining or analytics techniques is the appropriate action.

Clustering is defined as “an unsupervised process that partitions data into groups (clusters) based on similarity without preassigned labels.” K-Means is the canonical unsupervised clustering algorithm, iteratively assigning points to K centroids to minimize within-cluster variance. This makes K-Means the simplest and most direct choice for grouping data in an anomaly-detection context.

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Under CPMAI Phase III: Data Preparation, the Data Format task includes “Data anonymization” as a core activity to remove or mask PII when it is not required for modeling, thereby protecting privacy while retaining data utility.

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Neural Networks—especially deep architectures—typically require extensive computational resources, longer training times, and higher infrastructure costs compared to simpler methods. In contrast, algorithms like Naive Bayes train very quickly on large datasets, and Gaussian Mixture Models or SVMs have more moderate training complexity and infrastructure demands. Therefore, given strict constraints on training time, cost, and complexity, Neural Networks are the least suitable choice.

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